/level-up

name: level-up
description: Use weekly to find and ship one new automation. Walks the 3Ms interview — Mindset (find the candidate) → Method (scope one) → Machine (build it). Trigger on "let's level up", "what should I automate next", "find me leverage this week", or as a Friday ritual. One run = one shipped artifact.

> *Inspired by Nate Herk's "The Three Ms of AI"™ framework ("The Three Ms of AI" is his trademark).*

What this skill does

Walks the user through the 3Ms each week to surface and ship one new automation. **One interview = one artifact.** It also installs the 3Ms framework into the user's head over time — after 4-6 runs, the user starts spotting opportunities mid-week without prompting because the questions have become internal defaults.

This is the brain-rewire mechanism. The kit doesn't need cron jobs to anchor behavior; it needs `/level-up` running every Friday.

What `/level-up` is NOT

• Not `/audit`. `/audit` is structural ("is the AIOS built right?"). `/level-up` is functional ("what business leverage am I missing?"). Run `/audit` first if structure is messy.
• Not a multi-candidate planner. One run = one shipped artifact.
• Not a coach. The user does the thinking. The skill conducts the interview.

When `/level-up` runs

• **First run: Day 14.** After the user has connected ≥1 MCP/script and run `/audit` once. Earlier yields trivial output.
• **Cadence: weekly, Friday afternoon.** Review the week, surface one automation, ship Monday.
• **On-demand any time.** Mid-week if a manual task itches.

Inputs the skill reads

• `context/priorities.md` — what the user said matters
• `context/about-me.md` — top_pain, role
• `connections.md` — what's reachable, by what mechanism
• `references/3ms-framework.md` — the framework (used to quote principles back)
• `decisions/log.md` — recent decisions (what's already shipped or considered)
• `.claude/skills/*/SKILL.md` frontmatter — what capabilities exist
• Recent `audits/audit-{date}.md` if present

Execution — three phases

Phase 1 — Mindset interview (find the candidate)

Surface 1-3 candidates ranked by leverage. Ask these in order, conversationally:

1. *"Walk me through your week. What did you do 3+ times?"* (frequency) 2. *"Anything that felt manual, boring, or copy-paste?"* (drudgery) 3. *"Anything where you thought 'a smart intern could handle this'?"* (delegation) 4. *"If 500 new clients showed up tomorrow, what would break first?"* (constraint) 5. *"What would give you 500 more clients tomorrow?"* (growth lever)

Quote relevant Mindset principles when they fit:

• *"Sounds like the Default Shift applies — to what extent could AI be leveraged here?"*
• *"This is the Function Breakdown — you're not automating the whole job, just this one piece."*
• *"AI is better than you think and improving faster than you think. If it couldn't do this last quarter, it might be ready now."*

**Output of Phase 1:** numbered list of 1-3 candidate opportunities, one-line "why this is leverage" per candidate. Ask: *"Pick one to scope."*

Phase 2 — Method interview (scope one)

User picks one candidate. Walk the 5-step Method pipeline:

**Step 1 — Find the constraint.** Which bottleneck does this solve, or which growth lever does it open? Tie back to Phase 1 answers.

**Step 2 — EAD: Eliminate / Automate / Delegate.**

• **Eliminate first:** *"What happens if we just stop doing this?"* If the answer is "nothing breaks" → skill exits cheerfully. *"Don't automate waste."* This is a win, log to `decisions/log.md` and stop.
• **Automate second:** apply 60/30/10 framing. ~60% deterministic, ~30% AI-assisted, ~10% manual.
• **Delegate third:** if too complex/variable/judgment-heavy → suggest a person. Skill exits with a delegation suggestion, log it.

**Step 3 — Map the process.** Five elements:

• Trigger (what kicks it off)
• Data sources (where info comes from)
• Data transformations (how data changes shape)
• Decision points (where it branches)
• Destination (where output goes)

If the user can't articulate any of the five: *"If you can't explain it to a person, you can't explain it to an AI. Sketch it on paper first, then come back."* Skill stops.

**Step 4 — Pick the autonomy level.**

| Level | Name | What happens | |---|---|---| | L0 | Manual | No AI | | L1 | Suggested | AI suggests, human decides every step | | L2 | Drafted | AI drafts, human reviews and edits | | L3 | Supervised | AI runs, human validates periodically | | L4 | Autonomous | AI handles end-to-end |

**Default = lowest level that solves the problem.** Push back on L4 unless the user has explicitly run lower levels first. *"Workflows beat agents. If a decision doesn't HAVE to be made by AI, don't let AI make it."*

**Step 5 — Tie to a KPI.** Which of the Three Buckets does this move?

• More customers
• More value per customer
• Less cost

Plus a specific metric (response time, error rate, conversion rate, time-to-completion). **If the user can't name a bucket and a metric, skill stops.** *"If your automation doesn't move a number, why are you building it?"*

**Output of Phase 2:** scoped automation spec written to `decisions/log.md` as a dated entry with all five answers + autonomy level + KPI. Durable record of what was decided and why.

Phase 3 — Machine handoff (build it)

Ask: *"How do you want to ship this?"* Options ordered by Boring-is-Beautiful default:

1. **Prompt-only** — saved prompt template the user runs by hand. Zero infrastructure. Highest manual involvement. 2. **Deterministic skill** — SKILL.md that runs a script (no AI step). Best for transformations with clear rules. 3. **AI-assisted skill** — SKILL.md with one AI call inside. Drafts, classifies, summarizes. 4. **Sub-agent** — multi-step agent. Last resort. Only if the work genuinely needs reasoning + tool use.

**Default selected = highest non-AI option that solves the problem.** User has to explicitly choose more autonomy.

Once chosen, route to the appropriate scaffolder: